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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "最好使用V100的高RAM配置"
      ],
      "metadata": {
        "id": "iYXnu6ac-9dx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6QIuNS4YvKf",
        "outputId": "99039584-4b7c-401a-f81a-6647db93f668"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ChatGLM3'...\n",
            "remote: Enumerating objects: 1559, done.\u001b[K\n",
            "remote: Counting objects: 100% (665/665), done.\u001b[K\n",
            "remote: Compressing objects: 100% (132/132), done.\u001b[K\n",
            "remote: Total 1559 (delta 573), reused 533 (delta 533), pack-reused 894 (from 2)\u001b[K\n",
            "Receiving objects: 100% (1559/1559), 17.75 MiB | 16.74 MiB/s, done.\n",
            "Resolving deltas: 100% (897/897), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/THUDM/ChatGLM3"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Inm7TvMZjJe",
        "outputId": "46f19a3b-9b6d-4788-ea08-e41988e7fe0f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ChatGLM3  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ChatGLM3\n",
        "!cat requirements.txt\n",
        "# !pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9hpMKsrorUR-",
        "outputId": "fb74617d-0bc6-4997-a45e-501aa669bb8b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ChatGLM3\n",
            "# basic requirements\n",
            "\n",
            "transformers==4.40.0\n",
            "cpm_kernels>=1.0.11\n",
            "torch>=2.3.0\n",
            "vllm>=0.4.2\n",
            "gradio>=4.26.0\n",
            "sentencepiece>=0.2.0\n",
            "sentence_transformers>=2.7.0\n",
            "accelerate>=0.29.2\n",
            "streamlit>=1.33.0\n",
            "fastapi>=0.110.0\n",
            "loguru~=0.7.2\n",
            "mdtex2html>=1.3.0\n",
            "latex2mathml>=3.77.0\n",
            "jupyter_client>=8.6.1\n",
            "\n",
            "# for openai demo\n",
            "openai>=1.30.1\n",
            "pydantic>=2.7.1\n",
            "sse-starlette>=2.1.0\n",
            "uvicorn>=0.29.0\n",
            "timm>=0.9.16\n",
            "tiktoken>=0.6.0\n",
            "\n",
            "# for langchain demo\n",
            "\n",
            "langchain>=0.2.1\n",
            "langchain_community>=0.2.0\n",
            "langchainhub>=0.1.15\n",
            "arxiv>=2.1.0"
          ]
        }
      ]
    },
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      "source": [
        "\n",
        "!pip install transformers==4.39.3\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"THUDM/chatglm3-6b\", trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(\"THUDM/chatglm3-6b\", trust_remote_code=True, device='cuda')\n",
        "model = model.eval()\n"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers==4.39.3\n",
            "  Downloading transformers-4.39.3-py3-none-any.whl.metadata (134 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/134.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (3.18.0)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (2.0.2)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (2.32.3)\n",
            "Collecting tokenizers<0.19,>=0.14 (from transformers==4.39.3)\n",
            "  Downloading tokenizers-0.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (0.5.3)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.39.3) (4.14.1)\n",
            "Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.39.3) (1.1.5)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers==4.39.3) (2025.7.14)\n",
            "Downloading transformers-4.39.3-py3-none-any.whl (8.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.8/8.8 MB\u001b[0m \u001b[31m113.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tokenizers-0.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m60.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tokenizers, transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.21.2\n",
            "    Uninstalling tokenizers-0.21.2:\n",
            "      Successfully uninstalled tokenizers-0.21.2\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.53.2\n",
            "    Uninstalling transformers-4.53.2:\n",
            "      Successfully uninstalled transformers-4.53.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "sentence-transformers 4.1.0 requires transformers<5.0.0,>=4.41.0, but you have transformers 4.39.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed tokenizers-0.15.2 transformers-4.39.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/file_download.py:943: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82c24bc619e147c9a34ce8fce1ffd9fe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenization_chatglm.py: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e3424fe32b04b6b9df3def10af0c316"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm3-6b:\n",
            "- tokenization_chatglm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
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              "model_id": "2c20b843dfb94148beb8fd6d02e13357"
            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "5bae86be5f714fd1a6406aa6826cbead"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.THUDM.chatglm3-6b.e9e0406d062cdb887444fe5bd546833920abd4ac.tokenization_chatglm:Setting eos_token is not supported, use the default one.\n",
            "WARNING:transformers_modules.THUDM.chatglm3-6b.e9e0406d062cdb887444fe5bd546833920abd4ac.tokenization_chatglm:Setting pad_token is not supported, use the default one.\n",
            "WARNING:transformers_modules.THUDM.chatglm3-6b.e9e0406d062cdb887444fe5bd546833920abd4ac.tokenization_chatglm:Setting unk_token is not supported, use the default one.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adc87f51e1bc46ffb985fbc74ddc288c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "configuration_chatglm.py: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7fcb99a00c6745a9af7b30bdf08fc34b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm3-6b:\n",
            "- configuration_chatglm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_chatglm.py: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd9388db356d418ea643a39d27d840f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "quantization.py: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6bfcd0274544463f9cdf8fcf75735b38"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm3-6b:\n",
            "- quantization.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm3-6b:\n",
            "- modeling_chatglm.py\n",
            "- quantization.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/file_download.py:943: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "3b4f02a05c0c46c083ab3826eac68bd9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
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          "metadata": {}
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          },
          "metadata": {}
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "3660b7d923de416e8256f458f1e3799e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ca72c3af87942d682857cfe253c9d53"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2c8e7wKbddw2",
        "outputId": "6a5e0ae8-d662-42fd-e584-5145acb0f4b3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ChatGLMForConditionalGeneration(\n",
              "  (transformer): ChatGLMModel(\n",
              "    (embedding): Embedding(\n",
              "      (word_embeddings): Embedding(65024, 4096)\n",
              "    )\n",
              "    (rotary_pos_emb): RotaryEmbedding()\n",
              "    (encoder): GLMTransformer(\n",
              "      (layers): ModuleList(\n",
              "        (0-27): 28 x GLMBlock(\n",
              "          (input_layernorm): RMSNorm()\n",
              "          (self_attention): SelfAttention(\n",
              "            (query_key_value): Linear(in_features=4096, out_features=4608, bias=True)\n",
              "            (core_attention): CoreAttention(\n",
              "              (attention_dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (dense): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          )\n",
              "          (post_attention_layernorm): RMSNorm()\n",
              "          (mlp): MLP(\n",
              "            (dense_h_to_4h): Linear(in_features=4096, out_features=27392, bias=False)\n",
              "            (dense_4h_to_h): Linear(in_features=13696, out_features=4096, bias=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (final_layernorm): RMSNorm()\n",
              "    )\n",
              "    (output_layer): Linear(in_features=4096, out_features=65024, bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "for idx, (key, value) in enumerate(model.named_parameters()):\n",
        "    print(f\"{key:^40} paramerters num:  {value.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6UwaEgwzfWW2",
        "outputId": "4aa77ee8-daad-4ee9-aae9-f1a6690a124a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "transformer.embedding.word_embeddings.weight paramerters num:  torch.Size([65024, 4096])\n",
            "transformer.encoder.layers.0.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.0.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.0.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.0.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.0.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.0.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.0.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.1.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.1.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.1.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.1.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.1.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.1.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.1.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.2.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.2.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.2.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.2.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.2.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.2.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.2.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.3.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.3.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.3.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.3.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.3.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.3.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.3.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.4.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.4.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.4.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.4.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.4.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.4.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.4.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.5.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.5.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.5.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.5.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.5.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.5.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.5.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.6.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.6.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.6.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.6.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.6.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.6.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.6.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.7.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.7.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.7.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.7.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.7.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.7.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.7.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.8.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.8.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.8.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.8.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.8.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.8.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.8.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.9.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.9.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.9.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.9.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.9.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.9.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.9.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.10.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.10.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.10.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.10.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.10.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.10.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.10.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.11.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.11.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.11.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.11.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.11.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.11.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.11.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.12.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.12.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.12.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.12.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.12.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.12.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.12.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.13.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.13.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.13.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.13.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.13.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.13.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.13.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.14.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.14.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.14.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.14.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.14.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.14.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.14.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.15.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.15.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.15.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.15.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.15.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.15.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.15.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.16.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.16.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.16.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.16.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.16.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.16.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.16.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.17.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.17.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.17.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.17.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.17.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.17.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.17.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.18.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.18.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.18.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.18.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.18.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.18.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.18.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.19.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.19.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.19.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.19.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.19.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.19.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.19.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.20.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.20.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.20.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.20.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.20.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.20.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.20.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.21.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.21.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.21.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.21.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.21.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.21.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.21.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.22.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.22.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.22.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.22.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.22.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.22.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.22.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.23.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.23.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.23.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.23.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.23.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.23.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.23.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.24.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.24.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.24.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.24.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.24.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.24.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.24.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.25.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.25.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.25.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.25.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.25.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.25.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.25.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.26.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.26.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.26.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.26.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.26.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.26.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.26.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.27.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.27.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.27.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.27.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.27.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.27.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.27.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.final_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "    transformer.output_layer.weight      paramerters num:  torch.Size([65024, 4096])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data=input(\"请输入:\")\n",
        "response,history = model.chat(tokenizer, 'AI太难学怎么办', history=[])\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBhQClVRZGXG",
        "outputId": "2bc2601a-b0d5-49ee-aa9a-32395f2cc809"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "学习AI（人工智能）可能会有一定的难度，但是请不要气馁。以下是一些建议，可以帮助你更好地学习和理解AI：\n",
            "\n",
            "1. 基础知识：确保你具备一定的数学和编程基础，这将有助于你更好地理解AI的概念。\n",
            "\n",
            "2. 学习资源：利用网络资源，如在线课程、博客、书籍等，来学习AI基础知识。有很多优秀的资源可以帮助你入门，例如：吴恩达的《深度学习专项课程》、李飞飞等的《CS231n：卷积神经网络与视觉识别》等。\n",
            "\n",
            "3. 实践：实践是巩固理论知识的最好方法。通过实际项目或编程挑战，将所学知识应用到实际问题中。这将帮助你更好地理解AI算法的工作原理和实际应用。\n",
            "\n",
            "4. 建立自己的项目：从简单的项目开始，如图像分类、文本分类等，逐步提高难度。这将帮助你将所学知识应用于实际问题，并培养你的动手能力。\n",
            "\n",
            "5. 交流与讨论：加入AI相关的社区和讨论组，与他人分享和讨论学习经验。这将有助于你发现自己的不足之处，并从他人的经验中获益。\n",
            "\n",
            "6. 持续学习：AI领域发展迅速，不断有新的理论和技术涌现。保持对AI的热情，持续学习，跟上领域的发展趋势。\n",
            "\n",
            "最后，学习AI需要时间和耐心。不要气馁，坚持下去，相信你一定能够掌握AI技能并取得成功。祝你学习顺利！\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#挂载谷歌云盘\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "6AF3_UrijO8x"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#查看huggingface下载的模型，并复制到谷歌云盘里\n",
        "import os\n",
        "# os.listdir('/root/.cache/huggingface/hub/models--THUDM--chatglm3-6b')\n",
        "# #os.cp 上面路径内容到 /content/drive/下面\n",
        "# os.system('cp -r /root/.cache/huggingface/hub/models--THUDM--chatglm3-6b /content/drive/MyDrive/')"
      ],
      "metadata": {
        "id": "7s6Zv6k7kB08"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}